In this section, we describe the MRI data characteristics and present the explicit quantification of density-based imaging phenotypes. MRI scans are a rich source of imaging data as they provide a wide range of imaging contrasts. Consequently, different tissue characteristics in the brain are highlighted differently by the various MRI sequences. Primary MRI sequences include (i) native (T1), (ii) post-contrast T1-weighted (T1Gd), (iii) T2-weighted (T2), and (iv) T2 fluid attenuated inversion recovery (FLAIR). The utility of these imaging sequences in providing complementary information can be seen in Figure S1, where the tumor sub-regions appear with varying contrasts. MRI scans typically include data on all four imaging sequences and segmentation labels that generate a mask indicating the tumor and non-tumor regions in the MRI scan (top-left panel in Figure REF ). We specifically consider MRI scans of 65 LGG subjects obtained from The Cancer Imaging Archive (TCIA) [1]}. Further details about the data are provided in Section . Each MRI scan is structured as a 3D array where the third axis corresponds to the different axial slices of the image. Hence, we have four 3D arrays, each corresponding to one of the four imaging sequences, and an additional 3D array for the accompanying segmentation mask. All four MRI scans, and the segmentation mask, have a voxel-to-voxel correspondence which allows for clear specification of the tumor region. In Figure S1, we show an example of the axial slice from a brain MRI for a subject with LGG for each of the four imaging sequences. The region inside the red boundary overlaid on these images indicates the segmented tumor region. To integrate MRI data into our modeling, we first construct imaging phenotypes (or radiomic phenotypes) based on the voxel intensities of the MRI scans.
